knitr::opts_chunk$set(echo = FALSE,cache = TRUE)
library(xlsx)
library(ggplot2)
## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang
library(gplots)
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
library(gridExtra)
library(corrplot)
## corrplot 0.84 loaded
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:gridExtra':
## 
##     combine
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(png)
library(grid)
library(heatmaply)
## Loading required package: plotly
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
## Loading required package: viridis
## Loading required package: viridisLite
## Registered S3 method overwritten by 'seriation':
##   method         from 
##   reorder.hclust gclus
## 
## ======================
## Welcome to heatmaply version 0.16.0
## 
## Type citation('heatmaply') for how to cite the package.
## Type ?heatmaply for the main documentation.
## 
## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
## Or contact: <tal.galili@gmail.com>
## ======================
## Warning: NAs introduced by coercion
##      Tree.ID Allocation Column Row Rep. measure Height Flower Flower.Level
## 840    IN4E4         F1      2  20    N       7      M      N            0
## 1363   IN4FM         F1      3  17    N      11      H      N            0
##         Chl  Flav  Anth Height08 Height09
## 840  18.511 1.401 0.370      138      234
## 1363 59.122 1.640 0.483      166      245
## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`

Tree Plots

Anthocyanin

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).

## Warning: Removed 21 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 21 rows containing non-finite values (stat_bin).
## Warning: Removed 21 rows containing non-finite values (stat_density).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##              Df  Sum Sq   Mean Sq F value Pr(>F)
## F1$Tree.ID  158 0.26483 0.0016762  0.9733 0.5779
## Residuals  1631 2.80887 0.0017222

  • R.squared
## [1] 0.08616099

Chlorophyll

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).

## Warning: Removed 21 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 21 rows containing non-finite values (stat_bin).
## Warning: Removed 21 rows containing non-finite values (stat_density).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Chl5
##              Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID  158  23815  150.73  0.6775  0.999
## Residuals  1631 362856  222.47

  • R.squared
## [1] 0.06158903

Flavonol

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).

## Warning: Removed 21 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 21 rows containing non-finite values (stat_bin).
## Warning: Removed 21 rows containing non-finite values (stat_density).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Flav5
##              Df Sum Sq  Mean Sq F value    Pr(>F)    
## F1$Tree.ID  158 14.876 0.094149  1.8118 1.856e-08 ***
## Residuals  1631 84.754 0.051965                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.149308

Height 2018

## Warning: Removed 89 rows containing non-finite values (stat_boxplot).

## Warning: Removed 8 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 8 rows containing non-finite values (stat_bin).
## Warning: Removed 8 rows containing non-finite values (stat_density).

  • ANOVA
## Warning in anova.lm(mod181): ANOVA F-tests on an essentially perfect fit
## are unreliable
## Analysis of Variance Table
## 
## Response: F1$H185
##              Df Sum Sq Mean Sq    F value    Pr(>F)    
## F1$Tree.ID  171 418774    2449 3.9594e+27 < 2.2e-16 ***
## Residuals  1550      0       0                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: not plotting observations with leverage one:
##   306, 1600, 1609, 1610, 1611, 1614, 1617, 1622, 1630, 1642, 1644, 1653, 1661, 1680, 1681, 1687, 1690, 1698, 1722

## Warning: not plotting observations with leverage one:
##   306, 1600, 1609, 1610, 1611, 1614, 1617, 1622, 1630, 1642, 1644, 1653, 1661, 1680, 1681, 1687, 1690, 1698, 1722

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

  • R.squared
## [1] 1

Height 2019

## Warning: Removed 90 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 9 rows containing non-finite values (stat_bin).
## Warning: Removed 9 rows containing non-finite values (stat_density).

  • ANOVA
## Warning in anova.lm(mod191): ANOVA F-tests on an essentially perfect fit
## are unreliable
## Analysis of Variance Table
## 
## Response: F1$H195
##              Df Sum Sq Mean Sq  F value    Pr(>F)    
## F1$Tree.ID  170 429289  2525.2 3.72e+27 < 2.2e-16 ***
## Residuals  1550      0     0.0                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: not plotting observations with leverage one:
##   1599, 1610, 1613, 1616, 1620, 1621, 1629, 1641, 1643, 1652, 1660, 1679, 1680, 1686, 1689, 1697, 1721

## Warning: not plotting observations with leverage one:
##   1599, 1610, 1613, 1616, 1620, 1621, 1629, 1641, 1643, 1652, 1660, 1679, 1680, 1686, 1689, 1697, 1721

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

  • R.squared
## [1] 1

Height Difference 2018-2019

## Warning: Removed 90 rows containing non-finite values (stat_boxplot).

## Warning: Removed 9 rows containing missing values (geom_point).

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 9 rows containing non-finite values (stat_bin).
## Warning: Removed 9 rows containing non-finite values (stat_density).

  • Anova
## Warning in anova.lm(modHD1): ANOVA F-tests on an essentially perfect fit
## are unreliable
## Analysis of Variance Table
## 
## Response: F1$HDiff
##              Df Sum Sq Mean Sq    F value    Pr(>F)    
## F1$Tree.ID  170 382107  2247.7 6.3777e+27 < 2.2e-16 ***
## Residuals  1550      0     0.0                         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: not plotting observations with leverage one:
##   1599, 1610, 1613, 1616, 1620, 1621, 1629, 1641, 1643, 1652, 1660, 1679, 1680, 1686, 1689, 1697, 1721

## Warning: not plotting observations with leverage one:
##   1599, 1610, 1613, 1616, 1620, 1621, 1629, 1641, 1643, 1652, 1660, 1679, 1680, 1686, 1689, 1697, 1721

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

  • R.squared
## [1] 1

Leaf Height Plots

## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`

## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`

## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`

## Warning: Factor `Height` contains implicit NA, consider using
## `forcats::fct_explicit_na`

Anthocyanin

## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).

  • ANOVA Anthocyanin and Collection Height
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq   Mean Sq F value    Pr(>F)    
## F1$Height    2 0.04916 0.0245811  14.523 5.537e-07 ***
## Residuals 1787 3.02454 0.0016925                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Collection Height
## [1] 0.0159945
  • ANOVA Anthocyanin High vs Mid Heights
## Analysis of Variance Table
## 
## Response: HH$Anth
##            Df   Sum Sq    Mean Sq F value Pr(>F)
## HM$Anth     1 0.000215 0.00021465  0.2884  0.592
## Residuals 157 0.116846 0.00074425

  • R.squared Anthocyanin High vs Mid Heights
## [1] 0.001833689
  • ANOVA Anthocyanin Low vs Mid Heights
## Analysis of Variance Table
## 
## Response: HL$Anth
##            Df   Sum Sq    Mean Sq F value Pr(>F)
## HM$Anth     1 0.000172 0.00017166  0.3687 0.5446
## Residuals 157 0.073097 0.00046558

  • R.squared Anthocyanin Low vs Mid Heights
## [1] 0.002342859
  • ANOVA Anthocyanin High vs Low Heights
## Analysis of Variance Table
## 
## Response: HH$Anth
##            Df   Sum Sq    Mean Sq F value Pr(>F)
## HL$Anth     1 0.000118 0.00011794  0.1583 0.6912
## Residuals 157 0.116943 0.00074486

  • R.squared Anthocyanin High vs Low Heights
## [1] 0.001007545

Chlorophyll

## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).

  • ANOVA Chlorophyll and Collection Height
## Analysis of Variance Table
## 
## Response: F1$Chl5
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height    2    791  395.31  1.8307 0.1606
## Residuals 1787 385880  215.94

  • R.squared Collection Height
## [1] 0.0159945
  • ANOVA Chlorophyll High vs Mid Heights
## Analysis of Variance Table
## 
## Response: HH$Chl
##            Df  Sum Sq Mean Sq F value  Pr(>F)  
## HM$Chl      1   504.5  504.50   5.982 0.01556 *
## Residuals 157 13240.8   84.34                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Chlorophyll High vs Mid Heights
## [1] 0.001833689
  • ANOVA Chlorophyll Low vs Mid Heights
## Analysis of Variance Table
## 
## Response: HL$Chl
##            Df Sum Sq Mean Sq F value  Pr(>F)  
## HM$Chl      1  181.3 181.326  3.2087 0.07518 .
## Residuals 157 8872.3  56.512                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Chlorophyll Low vs Mid Heights
## [1] 0.002342859
  • ANOVA Chlorophyll High vs Low Heights
## Analysis of Variance Table
## 
## Response: HH$Chl
##            Df  Sum Sq Mean Sq F value   Pr(>F)   
## HL$Chl      1   630.2  630.24  7.5446 0.006723 **
## Residuals 157 13115.0   83.54                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Chlorophyll High vs Low Heights
## [1] 0.001007545

Flavonol

## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).

  • ANOVA Flavonol and Collection Height
## Analysis of Variance Table
## 
## Response: F1$Flav5
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Height    2  0.972 0.48589  8.8009 0.0001572 ***
## Residuals 1787 98.658 0.05521                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Collection Height
## [1] 0.009753874
  • ANOVA Flavonol High vs Mid Heights
## Analysis of Variance Table
## 
## Response: HH$Flav
##            Df Sum Sq  Mean Sq F value Pr(>F)  
## HM$Flav     1 0.0540 0.053997  3.1493 0.0779 .
## Residuals 157 2.6919 0.017146                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flavonol High vs Mid Heights
## [1] 0.001833689
  • ANOVA Flavonol Low vs Mid Heights
## Analysis of Variance Table
## 
## Response: HL$Flav
##            Df Sum Sq  Mean Sq F value  Pr(>F)  
## HM$Flav     1 0.1518 0.151825   5.739 0.01777 *
## Residuals 157 4.1534 0.026455                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flavonol Low vs Mid Heights
## [1] 0.002342859
  • ANOVA Flavonol High vs Low Heights
## Analysis of Variance Table
## 
## Response: HH$Flav
##            Df  Sum Sq   Mean Sq F value Pr(>F)
## HL$Flav     1 0.00651 0.0065076   0.373 0.5423
## Residuals 157 2.73937 0.0174482

  • R.squared Flavonol High vs Low Heights
## [1] 0.001007545

Row Plots

Anthocyanin

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq   Mean Sq F value Pr(>F)
## F1$Row      49 0.00086 0.0000176    0.01      1
## Residuals 1740 3.07284 0.0017660

  • R.squared
## [1] 0.000280517

Chlorophyll

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Chl5
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row      49     48   0.977  0.0044      1
## Residuals 1740 386623 222.197

  • R.squared
## [1] 0.0001237819

Flavonol

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Flav5
##             Df Sum Sq  Mean Sq F value Pr(>F)
## F1$Row      49  0.204 0.004166  0.0729      1
## Residuals 1740 99.426 0.057141

  • R.squared
## [1] 0.002048742

Height 2018

## Warning: Removed 89 rows containing non-finite values (stat_boxplot).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$H185
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Row      49  24288  495.68  2.1009 1.659e-05 ***
## Residuals 1672 394486  235.94                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.05799867

Height 2019

## Warning: Removed 90 rows containing non-finite values (stat_boxplot).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$H195
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Row      49  36836  751.76  3.2009 1.777e-12 ***
## Residuals 1671 392453  234.86                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.08580726

Height Difference 2018-2019

## Warning: Removed 90 rows containing non-finite values (stat_boxplot).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$HDiff
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row      49   2266   46.25  0.2035      1
## Residuals 1671 379841  227.31

+R.squared

## [1] 0.00593089

Column Plots

Anthocyanin

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq    Mean Sq F value Pr(>F)
## F1$Column    3 0.00038 0.00012737   0.074 0.9739
## Residuals 1786 3.07332 0.00172078

  • R.squared
## [1] 0.0001243198

Chlorophyll

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Chl5
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column    3    198  65.844  0.3043 0.8223
## Residuals 1786 386473 216.390

  • R.squared
## [1] 0.0005108494

Flavonol

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Flav5
##             Df Sum Sq  Mean Sq F value Pr(>F)
## F1$Column    3  0.012 0.004060  0.0728 0.9746
## Residuals 1786 99.618 0.055777

  • R.squared
## [1] 0.0001222632

Height 2018

## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 89 rows containing non-finite values (stat_summary).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$H185
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Column    3  15817  5272.4  22.479 2.826e-14 ***
## Residuals 1718 402957   234.6                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.03776989

Height 2019

## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$H195
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Column    3  23664  7887.8  33.389 < 2.2e-16 ***
## Residuals 1717 405626   236.2                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.05512251

Height Difference 2018-2019

## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$HDiff
##             Df Sum Sq Mean Sq F value  Pr(>F)  
## F1$Column    3   1958  652.75  2.9483 0.03172 *
## Residuals 1717 380149  221.40                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.005124903

Flowering and Flowering Levels

Flowering refers to presence or not of flowers, Flower Level refers to a measure relating to the number of flowers present (0 = no flowers, 1 = 1-10, 2 = 10-20, 3 = 20+)

Anthocyanin

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Level
## Analysis of Variance Table
## 
## Response: F1$Anth5
##                   Df Sum Sq    Mean Sq F value Pr(>F)
## F1$Flower.Level    3 0.0028 0.00093363   0.543 0.6529
## Residuals       1786 3.0709 0.00171943

  • R.squared Flowering Level
## [1] 0.0009112453
  • T.tests comparing Flowering Levels and Anthocyanin
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Anth and FLWR1$Anth
## t = 0.025664, df = 17.375, p-value = 0.9798
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.008423762  0.008631570
## sample estimates:
##     mean of x     mean of y 
## -0.0001864908 -0.0002903948
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Anth and FLWR2$Anth
## t = 0.2922, df = 10.049, p-value = 0.7761
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.006353989  0.008273500
## sample estimates:
##     mean of x     mean of y 
## -0.0001864908 -0.0011462463
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Anth and FLWR3$Anth
## t = -1.1298, df = 10.172, p-value = 0.2845
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.014848706  0.004841807
## sample estimates:
##     mean of x     mean of y 
## -0.0001864908  0.0048169586
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$Anth and FLWR2$Anth
## t = 0.1717, df = 22.843, p-value = 0.8652
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.009459314  0.011171017
## sample estimates:
##     mean of x     mean of y 
## -0.0002903948 -0.0011462463
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$Anth and FLWR3$Anth
## t = -0.88018, df = 21.286, p-value = 0.3886
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.017164673  0.006949967
## sample estimates:
##     mean of x     mean of y 
## -0.0002903948  0.0048169586
## 
##  Welch Two Sample t-test
## 
## data:  FLWR2$Anth and FLWR3$Anth
## t = -1.1256, df = 15.954, p-value = 0.277
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.017196954  0.005270544
## sample estimates:
##    mean of x    mean of y 
## -0.001146246  0.004816959
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Presence
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq    Mean Sq F value Pr(>F)
## F1$Flower    1 0.00039 0.00039351  0.2289 0.6324
## Residuals 1788 3.07330 0.00171885

  • R.squared Flowering Presence
## [1] 0.0001280242

Chlorophyll

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Level
## Analysis of Variance Table
## 
## Response: F1$Chl5
##                   Df Sum Sq Mean Sq F value  Pr(>F)  
## F1$Flower.Level    3   1461  487.00  2.2579 0.07985 .
## Residuals       1786 385210  215.68                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flowering Level
## [1] 0.003778413
  • T.tests comparing Flowering Levels and Chlorophyll
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Chl and FLWR1$Chl
## t = -1.3501, df = 18.737, p-value = 0.1931
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.4526591  0.7464858
## sample estimates:
##  mean of x  mean of y 
## -0.2203429  1.1327437
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Chl and FLWR2$Chl
## t = -3.0493, df = 9.63, p-value = 0.01279
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.7911270 -0.8863768
## sample estimates:
##  mean of x  mean of y 
## -0.2203429  3.1184090
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Chl and FLWR3$Chl
## t = 0.70751, df = 10.428, p-value = 0.4948
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.844508  3.574981
## sample estimates:
##  mean of x  mean of y 
## -0.2203429 -1.0855790
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$Chl and FLWR2$Chl
## t = -1.4075, df = 19.522, p-value = 0.175
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.9330444  0.9617139
## sample estimates:
## mean of x mean of y 
##  1.132744  3.118409
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$Chl and FLWR3$Chl
## t = 1.4669, df = 19.505, p-value = 0.1583
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.9413134  5.3779589
## sample estimates:
## mean of x mean of y 
##  1.132744 -1.085579
## 
##  Welch Two Sample t-test
## 
## data:  FLWR2$Chl and FLWR3$Chl
## t = 2.6688, df = 16.937, p-value = 0.01623
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.8795891 7.5283869
## sample estimates:
## mean of x mean of y 
##  3.118409 -1.085579
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Presence
## Analysis of Variance Table
## 
## Response: F1$Chl5
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower    1     19  18.847  0.0872 0.7679
## Residuals 1788 386652 216.248

  • R.squared Flowering Presence
## [1] 4.87422e-05

Flavonol

## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Levels
## Analysis of Variance Table
## 
## Response: F1$Flav5
##                   Df Sum Sq  Mean Sq F value  Pr(>F)  
## F1$Flower.Level    3  0.574 0.191220  3.4477 0.01606 *
## Residuals       1786 99.056 0.055463                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flowering Levels
## [1] 0.005757915
  • T.tests comparing Flowering Levels and Flavonol
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Flav and FLWR1$Flav
## t = -0.19352, df = 20.332, p-value = 0.8485
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.04944842  0.04104444
## sample estimates:
##     mean of x     mean of y 
## -0.0047609014 -0.0005589119
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Flav and FLWR2$Flav
## t = -2.2376, df = 8.9795, p-value = 0.05211
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1555838602  0.0008748158
## sample estimates:
##    mean of x    mean of y 
## -0.004760901  0.072593621
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$Flav and FLWR3$Flav
## t = -0.58781, df = 9.9953, p-value = 0.5697
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.10202255  0.05943198
## sample estimates:
##    mean of x    mean of y 
## -0.004760901  0.016534383
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$Flav and FLWR2$Flav
## t = -1.8689, df = 13.816, p-value = 0.08299
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.15721039  0.01090532
## sample estimates:
##     mean of x     mean of y 
## -0.0005589119  0.0725936208
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$Flav and FLWR3$Flav
## t = -0.42086, df = 14.853, p-value = 0.6799
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1037374  0.0695508
## sample estimates:
##     mean of x     mean of y 
## -0.0005589119  0.0165343828
## 
##  Welch Two Sample t-test
## 
## data:  FLWR2$Flav and FLWR3$Flav
## t = 1.1508, df = 16.999, p-value = 0.2658
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.04672184  0.15884031
## sample estimates:
##  mean of x  mean of y 
## 0.07259362 0.01653438
## Warning: Removed 21 rows containing non-finite values (stat_boxplot).
## Warning: Removed 21 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Presence
## Analysis of Variance Table
## 
## Response: F1$Flav5
##             Df Sum Sq  Mean Sq F value  Pr(>F)  
## F1$Flower    1   0.24 0.240050  4.3184 0.03784 *
## Residuals 1788  99.39 0.055587                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flowering Presence
## [1] 0.002409418

Height 2018

## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 89 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Levels
## Analysis of Variance Table
## 
## Response: F1$H185
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Flower.Level    3  11639  3879.6  16.371 1.718e-10 ***
## Residuals       1718 407135   237.0                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flowering Levels
## [1] 0.02779275
  • T.tests comparing Flowering Levels and 2018 Height
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$H18 and FLWR1$H18
## t = -1.6305, df = 17.703, p-value = 0.1207
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -18.48037   2.34066
## sample estimates:
## mean of x mean of y 
## -2.410131  5.659726
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$H18 and FLWR2$H18
## t = -0.81929, df = 6.4906, p-value = 0.4417
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -22.95103  11.27931
## sample estimates:
## mean of x mean of y 
## -2.410131  3.425728
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$H18 and FLWR3$H18
## t = -1.5807, df = 9.3853, p-value = 0.147
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -19.310608   3.365938
## sample estimates:
## mean of x mean of y 
## -2.410131  5.562204
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$H18 and FLWR2$H18
## t = 0.26453, df = 11.816, p-value = 0.7959
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -16.19812  20.66612
## sample estimates:
## mean of x mean of y 
##  5.659726  3.425728
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$H18 and FLWR3$H18
## t = 0.014376, df = 20.604, p-value = 0.9887
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -14.02649  14.22153
## sample estimates:
## mean of x mean of y 
##  5.659726  5.562204
## 
##  Welch Two Sample t-test
## 
## data:  FLWR2$H18 and FLWR3$H18
## t = -0.25133, df = 11.217, p-value = 0.8061
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -20.80233  16.52937
## sample estimates:
## mean of x mean of y 
##  3.425728  5.562204
## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 89 rows containing non-finite values (stat_summary).

  • ANOVA Flower Presence
## Analysis of Variance Table
## 
## Response: F1$H185
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## F1$Flower    1   2126 2125.89   8.776 0.003094 **
## Residuals 1720 416648  242.24                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flower Presence
## [1] 0.005076449

Height 2019

## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Level
## Analysis of Variance Table
## 
## Response: F1$H195
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Flower.Level    3   8766 2922.09  11.931 9.858e-08 ***
## Residuals       1717 420523  244.92                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flowering Level
## [1] 0.02042042
  • T.tests comparing Flowering Levels and 2019 Height
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$H19 and FLWR1$H19
## t = -1.7634, df = 18.363, p-value = 0.09447
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -17.725963   1.535942
## sample estimates:
## mean of x mean of y 
## -1.992529  6.102482
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$H19 and FLWR2$H19
## t = -0.12465, df = 6.2813, p-value = 0.9047
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -24.14341  21.77867
## sample estimates:
##  mean of x  mean of y 
## -1.9925285 -0.8101606
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$H19 and FLWR3$H19
## t = 0.3993, df = 9.2524, p-value = 0.6987
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -9.983022 14.284387
## sample estimates:
## mean of x mean of y 
## -1.992529 -4.143211
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$H19 and FLWR2$H19
## t = 0.66835, df = 8.7152, p-value = 0.5212
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -16.60158  30.42687
## sample estimates:
##  mean of x  mean of y 
##  6.1024818 -0.8101606
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$H19 and FLWR3$H19
## t = 1.5106, df = 18.391, p-value = 0.1479
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.982633 24.474019
## sample estimates:
## mean of x mean of y 
##  6.102482 -4.143211
## 
##  Welch Two Sample t-test
## 
## data:  FLWR2$H19 and FLWR3$H19
## t = 0.31093, df = 9.5696, p-value = 0.7625
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -20.69819  27.36429
## sample estimates:
##  mean of x  mean of y 
## -0.8101606 -4.1432112
## Warning: Removed 89 rows containing non-finite values (stat_boxplot).
## Warning: Removed 89 rows containing non-finite values (stat_summary).

  • ANOVA Flower Presence
## Analysis of Variance Table
## 
## Response: F1$H195
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$Flower    1    343  342.61   1.373 0.2415
## Residuals 1719 428947  249.53

  • R.squared Flower Presence
## [1] 0.0007980757

Height Difference 2018-2019

## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Level
## Analysis of Variance Table
## 
## Response: F1$HDiff
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Flower.Level    3  11680  3893.3  18.046 1.569e-11 ***
## Residuals       1717 370428   215.7                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flowering Level
## [1] 0.0305669
  • T.tests comparing Flowering Levels and 2018-19 Height Differences
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$HDiff and FLWR1$HDiff
## t = 0.075464, df = 22.089, p-value = 0.9405
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -6.774658  7.286433
## sample estimates:
## mean of x mean of y 
## 0.6986430 0.4427554
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$HDiff and FLWR2$HDiff
## t = 1.4817, df = 9.0037, p-value = 0.1725
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.598586 12.467650
## sample estimates:
## mean of x mean of y 
##  0.698643 -4.235889
## 
##  Welch Two Sample t-test
## 
## data:  FLWR0$HDiff and FLWR3$HDiff
## t = 2.0331, df = 9.4104, p-value = 0.07121
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.095699 21.903816
## sample estimates:
## mean of x mean of y 
##  0.698643 -9.705415
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$HDiff and FLWR2$HDiff
## t = 1.0879, df = 17.464, p-value = 0.2914
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.376308 13.733596
## sample estimates:
##  mean of x  mean of y 
##  0.4427554 -4.2358888
## 
##  Welch Two Sample t-test
## 
## data:  FLWR1$HDiff and FLWR3$HDiff
## t = 1.751, df = 14.32, p-value = 0.1013
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.256559 22.552900
## sample estimates:
##  mean of x  mean of y 
##  0.4427554 -9.7054153
## 
##  Welch Two Sample t-test
## 
## data:  FLWR2$HDiff and FLWR3$HDiff
## t = 0.94948, df = 12.734, p-value = 0.36
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.001849 17.940902
## sample estimates:
## mean of x mean of y 
## -4.235889 -9.705415
## Warning: Removed 90 rows containing non-finite values (stat_boxplot).
## Warning: Removed 90 rows containing non-finite values (stat_summary).

  • ANOVA Flowering Presence
## Analysis of Variance Table
## 
## Response: F1$HDiff
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$Flower    1   4246  4245.9  19.316 1.176e-05 ***
## Residuals 1719 377862   219.8                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared Flowering Presence
## [1] 0.01111171

Measure Correlations

Plots

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 21 rows containing non-finite values (stat_smooth).

## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).

## Warning: Removed 110 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).

## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).
## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).

## Warning: Removed 110 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).

## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).

## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).

## Warning: Removed 110 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 110 rows containing non-finite values (stat_smooth).

## Warning: Removed 110 rows containing missing values (geom_point).

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 90 rows containing non-finite values (stat_smooth).
## Warning: Removed 90 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 90 rows containing non-finite values (stat_smooth).

## Warning: Removed 90 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 90 rows containing non-finite values (stat_smooth).

## Warning: Removed 90 rows containing missing values (geom_point).

### Anthocyanin and Chlorophyll

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq Mean Sq F value    Pr(>F)    
## F1$Chl5      1 0.33366 0.33366  217.73 < 2.2e-16 ***
## Residuals 1788 2.74003 0.00153                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.1085545

Anthocyanin and Flavonol

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq  Mean Sq F value    Pr(>F)    
## F1$Flav5     1 0.08082 0.080820  48.283 5.148e-12 ***
## Residuals 1788 2.99288 0.001674                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.02629407

Anothcyanin and Height 2018

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq   Mean Sq F value Pr(>F)
## F1$H185      1 0.00199 0.0019900  1.1487  0.284
## Residuals 1699 2.94338 0.0017324

  • R.squared
## [1] 0.0006756401

Anthocyanin and Height 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df Sum Sq    Mean Sq F value Pr(>F)
## F1$H195      1 0.0000 0.00000073   4e-04 0.9836
## Residuals 1699 2.9454 0.00173359

  • R.squared
## [1] 2.490926e-07

Anthocyanin and Height Difference Between 2018 and 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq   Mean Sq F value Pr(>F)
## F1$HDiff     1 0.00209 0.0020945  1.2091 0.2717
## Residuals 1699 2.94327 0.0017324

  • R.squared
## [1] 0.0007111262

Chlorophyll and Flavonol

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Anth5
##             Df  Sum Sq  Mean Sq F value    Pr(>F)    
## F1$Flav5     1 0.08082 0.080820  48.283 5.148e-12 ***
## Residuals 1788 2.99288 0.001674                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.02629407

Chlorophyll and Height 2018

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Chl5
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$H185      1    414  413.56  1.9052 0.1677
## Residuals 1699 368794  217.07

  • R.squared
## [1] 0.001120119

Chlorophyll and Height 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Chl5
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$H195      1     78  78.221    0.36 0.5486
## Residuals 1699 369130 217.263

  • R.squared
## [1] 0.0002118618

Chlorophyll and Height Difference Between 2018 and 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Chl5
##             Df Sum Sq Mean Sq F value Pr(>F)
## F1$HDiff     1    142  141.85   0.653 0.4191
## Residuals 1699 369066  217.22

  • R.squared
## [1] 0.0003842085

Flavonol and Height 2018

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Flav5
##             Df Sum Sq  Mean Sq F value Pr(>F)
## F1$H185      1  0.073 0.073328  1.3145 0.2517
## Residuals 1699 94.773 0.055782

  • R.squared
## [1] 0.0007731187

Flavonol and Height 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Flav5
##             Df Sum Sq  Mean Sq F value  Pr(>F)  
## F1$H195      1  0.205 0.204977  3.6797 0.05525 .
## Residuals 1699 94.642 0.055704                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.002161139

Flavonol and Height Difference Between 2018 and 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$Flav5
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## F1$HDiff     1  0.582 0.58187  10.487 0.001225 **
## Residuals 1699 94.265 0.05548                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.006134827

Height 2018 and 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$H185
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$H195      1 125710  125710  740.77 < 2.2e-16 ***
## Residuals 1719 291720     170                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.3011528

Height 2018 and Height Difference Between 2018 and 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$H185
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$HDiff     1  89689   89689  470.42 < 2.2e-16 ***
## Residuals 1719 327741     191                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.2148603

Height 2019 and Height Difference Between 2018 and 2019

  • ANOVA
## Analysis of Variance Table
## 
## Response: F1$H195
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## F1$HDiff     1 101549  101549  532.62 < 2.2e-16 ***
## Residuals 1719 327741     191                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

  • R.squared
## [1] 0.2148603